OpenRaiser/Intern-Atlas huggingface dataset:
Improving Automatic Jazz Melody Generation by Transfer Learning Techniques
Venue: arXiv cs.SD 2019
Year: 2019

In this paper, we tackle the problem of transfer learning for Jazz automatic generation. Jazz is one of representative types of music, but the lack of Jazz data in the MIDI format hinders the construction of a generative model for Jazz. Transfer learning is an approach aiming to solve the problem of data insufficiency, so as to transfer the common feature from one domain to another. In view of its success in other machine learning problems, we investigate whether, and how much, it can help improve automatic music generation for under-resourced musical genres. Specifically, we use a recurrent variational autoencoder as the generative model, and use a genre-unspecified dataset as the source dataset and a Jazz-only dataset as the target dataset. Two transfer learning methods are evaluated using six levels of source-to-target data ratios. The first method is to train the model on the source dataset, and then fine-tune the resulting model parameters on the target dataset. The second method is to train the model on both the source and target datasets at the same time, but add genre labels to the latent vectors and use a genre classifier to improve Jazz generation. The evaluation results show that the second method seems to perform better overall, but it cannot take full advantage of the genre-unspecified dataset.
On Parameter Optimization of Product Codes for Iterative Bounded Distance Decoding with Scaled Reliability
Venue: arXiv cs.IT 2019
Year: 2019

We use density evolution to optimize the parameters of binary product codes (PCs) decoded based on the recently introduced iterative bounded distance decoding with scaled reliability. We show that binary PCs with component codes of 3-bit error correcting capability provide the best performance-complexity trade-off.
A Flexible Neural Renderer for Material Visualization
Venue: arXiv cs.GR 2019
Year: 2019

Photo realism in computer generated imagery is crucially dependent on how well an artist is able to recreate real-world materials in the scene. The workflow for material modeling and editing typically involves manual tweaking of material parameters and uses a standard path tracing engine for visual feedback. A lot of time may be spent in iterative selection and rendering of materials at an appropriate quality. In this work, we propose a convolutional neural network based workflow which quickly generates high-quality ray traced material visualizations on a shaderball. Our novel architecture allows for control over environment lighting and assists material selection along with the ability to render spatially-varying materials. Additionally, our network enables control over environment lighting which gives an artist more freedom and provides better visualization of the rendered material. Comparison with state-of-the-art denoising and neural rendering techniques suggests that our neural renderer performs faster and better. We provide a interactive visualization tool and release our training dataset to foster further research in this area.
Red-blue pebbling revisited: near optimal parallel matrix-matrix multiplication
Venue: arXiv cs.CC 2019
Year: 2019

We propose COSMA: a parallel matrix-matrix multiplication algorithm that is near communication-optimal for all combinations of matrix dimensions, processor counts, and memory sizes. The key idea behind COSMA is to derive an optimal (up to a factor of 0.03\% for 10MB of fast memory) sequential schedule and then parallelize it, preserving I/O optimality. To achieve this, we use the red-blue pebble game to precisely model MMM dependencies and derive a constructive and tight sequential and parallel I/O lower bound proofs. Compared to 2D or 3D algorithms, which fix processor decomposition upfront and then map it to the matrix dimensions, it reduces communication volume by up to $\sqrt{3}$ times. COSMA outperforms the established ScaLAPACK, CARMA, and CTF algorithms in all scenarios up to 12.8x (2.2x on average), achieving up to 88\% of Piz Daint's peak performance. Our work does not require any hand tuning and is maintained as an open source implementation.
DoS Protection through Credit Based Metering -- Simulation-Based Evaluation for Time-Sensitive Networking in Cars
Venue: arXiv cs.NI 2019
Year: 2019

Ethernet is the most promising solution to reduce complexity and enhance the bandwidth in the next generation in-car networks. Dedicated Ethernet protocols enable the real-time aspects in such networks. One promising candidate is the IEEE 802.1Q Time-Sensitive Networking protocol suite. Common Ethernet technologies, however, increases the vulnerability of the car infrastructure as they widen the attack surface for many components. In this paper proposes an IEEE 802.1Qci based algorithm that on the one hand, protects against DoS attacks by metering incoming Ethernet frames. On the other hand, it adapts to the behavior of the Credit Based Shaping algorithm, which was standardized for Audio/Video Bridging, the predecessor of Time-Sensitive Networking. A simulation of this proposed Credit Based Metering algorithm evaluates the concept.
SDN4CoRE: A Simulation Model for Software-Defined Networking for Communication over Real-Time Ethernet
Venue: arXiv cs.NI 2019
Year: 2019

Ethernet has become the next standard for automotive and industrial automation networks. Standard extensions such as IEEE 802.1Q Time-Sensitive Networking (TSN) have been proven to meet the real-time and robustness requirements of these environments. Augmenting the TSN switching by Software-Defined Networking functions promises additional benefits: A programming option for TSN devices can add much value to the resilience, security, and adaptivity of the environment. Network simulation allows to model highly complex networks before assembly and is an essential process for the design and validation of future networks. Still, a simulation environment that supports programmable real-time networks is missing. This paper fills the gap by sharing our simulation model for Software-Defined Networking for Communication over Real-Time Ethernet (SDN4CoRE) and present initial results in modeling programmable real-time networks. In a case study, we show that SDN4CoRE can simulate complex programmable real-time networks and allows for testing and verifying the programming of real-time devices.
An Introduction to Advanced Machine Learning : Meta Learning Algorithms, Applications and Promises
Venue: arXiv cs.LG 2019
Year: 2019

In [1, 2], we have explored the theoretical aspects of feature extraction optimization processes for solving largescale problems and overcoming machine learning limitations. Majority of optimization algorithms that have been introduced in [1, 2] guarantee the optimal performance of supervised learning, given offline and discrete data, to deal with curse of dimensionality (CoD) problem. These algorithms, however, are not tailored for solving emerging learning problems. One of the important issues caused by online data is lack of sufficient samples per class. Further, traditional machine learning algorithms cannot achieve accurate training based on limited distributed data, as data has proliferated and dispersed significantly. Machine learning employs a strict model or embedded engine to train and predict which still fails to learn unseen classes and sufficiently use online data. In this chapter, we introduce these challenges elaborately. We further investigate Meta-Learning (MTL) algorithm, and their application and promises to solve the emerging problems by answering how autonomous agents can learn to learn?.
Neural Code Search Evaluation Dataset
Venue: arXiv cs.SE 2019
Year: 2019

There has been an increase of interest in code search using natural language. Assessing the performance of such code search models can be difficult without a readily available evaluation suite. In this paper, we present an evaluation dataset consisting of natural language query and code snippet pairs, with the hope that future work in this area can use this dataset as a common benchmark. We also provide the results of two code search models ([1] and [6]) from recent work. The evaluation dataset is available at https://github.com/facebookresearch/Neural-Code-Search-Evaluation-Dataset
Consistency of community structure in complex networks
Venue: arXiv cs.SI 2019
Year: 2019

The most widely used techniques for community detection in networks, including methods based on modularity, statistical inference, and information theoretic arguments, all work by optimizing objective functions that measure the quality of network partitions. There is a good case to be made, however, that one should not look solely at the single optimal community structure under such an objective function, but rather at a selection of high-scoring structures. If one does this one typically finds that the resulting structures show considerable variation, and this has been taken as evidence that these community detection methods are unreliable, since they do not appear to give consistent answers. Here we argue that, upon closer inspection, the structures found are in fact consistent in a certain way. Specifically, we show that they can all be assembled from a set of underlying "building blocks", groups of network nodes that are usually found together in the same community. Different community structures correspond to different arrangements of blocks, but the blocks themselves are largely invariant. We propose an information theoretic method for discovering the building blocks in specific networks and demonstrate it with several example applications. We conclude that traditional community detection is not the failure some have suggested it is, and that in fact it gives a significant amount of insight into network structure, although perhaps not in exactly the way previously imagined.
Dimension independent bounds for general shallow networks
Venue: arXiv cs.LG 2019
Year: 2019

This paper proves an abstract theorem addressing in a unified manner two important problems in function approximation: avoiding curse of dimensionality and estimating the degree of approximation for out-of-sample extension in manifold learning. We consider an abstract (shallow) network that includes, for example, neural networks, radial basis function networks, and kernels on data defined manifolds used for function approximation in various settings. A deep network is obtained by a composition of the shallow networks according to a directed acyclic graph, representing the architecture of the deep network. In this paper, we prove dimension independent bounds for approximation by shallow networks in the very general setting of what we have called $G$-networks on a compact metric measure space, where the notion of dimension is defined in terms of the cardinality of maximal distinguishable sets, generalizing the notion of dimension of a cube or a manifold. Our techniques give bounds that improve without saturation with the smoothness of the kernel involved in an integral representation of the target function. In the context of manifold learning, our bounds provide estimates on the degree of approximation for an out-of-sample extension of the target function to the ambient space. One consequence of our theorem is that without the requirement of robust parameter selection, deep networks using a non-smooth activation function such as the ReLU, do not provide any significant advantage over shallow networks in terms of the degree of approximation alone.
A Short Course on Error-Correcting Codes
Venue: arXiv cs.IT 2019
Year: 2019

When digital data are transmitted over a noisy channel, it is important to have a mechanism allowing recovery against a limited number of errors. Normally, a user string of 0's and 1's, called bits, is encoded by adding a number of redundant bits to it. When the receiver attempts to reconstruct the original message sent, it starts by examining a possibly corrupted version of the encoded message, and then makes a decision. This process is called the decoding. The purpose of this course is giving an introduction to the theory and practice of error-correcting codes.
Integration of the Captive Portal paradigm with the 802.1X architecture
Venue: arXiv cs.NI 2019
Year: 2019

In a scenario where hotspot wireless networks are increasingly being used, and given the amount of sensitive information exchanged on Internet interactions, there is the need to implement security mechanisms that guarantee data confidentiality and integrity in such networks, as well as the authenticity of the hotspot providers. However, many hotspots today use Captive Portals, which rely on authentication through Web pages (thus, an application-level authentication approach) instead of a link-layer approach. The consequence of this is that there is no security in the wireless link to the hotspot (it has to be provided at upper protocol layers), and is cumbersome to manage wireless access profiles (we need special applications or browsers' add-ons to do that). This work exposes the weaknesses of the Captive Portals' paradigm, which does not follow a unique nor standard approach, and describes a solution that intends to suppress them, based on the 802.1X architecture. This solution uses a new EAP-compliant protocol that is able to integrate an HTTP-based registration or authentication with a Captive Portal within the 802.1X authentication framework.
Automated Fashion Size Normalization
Venue: arXiv cs.LG 2019
Year: 2019

The ability to accurately predict the fit of fashion items and recommend the correct size is key to reducing merchandise returns in e-commerce. A critical prerequisite of fit prediction is size normalization, the mapping of product sizes across brands to a common space in which sizes can be compared. At present, size normalization is usually a time-consuming manual process. We propose a method to automate size normalization through the use of salesdata. The size mappings generated from our automated approaches are comparable to human-generated mappings.
Handover Optimality in Heterogeneous Networks
Venue: arXiv cs.NI 2019
Year: 2019

This paper introduces a new theoretical framework for optimal handover procedures in heterogeneous networks by devising the novel fractional Gittins indices, which are dynamical priorities whose values can be statically associated to the decision states of evolving processes representing handover alternatives. The simple policy of activating at any time the one process currently at highest priority optimizes the bandwidth of a handover, if all other inactive processes remain idle. However, numerical evidence shows that in practice this condition can be relaxed for a wide range of handover models, because the bandwidth actually achieved by the policy never deviates for more than 12% from the optimally achievable bandwidth and remains in median within a deviation of 2% from this optimum.
Evolutionary Game for Hybrid Uplink NOMA with Truncated Channel Inversion Power Control
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we consider hybrid uplink nonorthogonal multiple access (NOMA) that can support more users by exploiting the notion of power-domain NOMA. In hybrid uplink NOMA, we do not consider centralized power control as a base station (BS) needs instantaneous channel state information (CSI) of all users which leads to a high signaling overhead. Rather, each user is allowed to perform power control under fading in accordance with a truncated channel inversion power control policy. Due to the lack of coordination of centralized power control, users in the same resource block compete for access. To analyze users' behavior, evolutionary game can be considered so that each user can choose transmission strategies to maximize payoff in hybrid uplink NOMA with power control. Evolutionarily stable strategy (ESS) is characterized with fixed costs as well as costs that depend on channel realizations, and it is also shown that hybrid uplink NOMA can provide a higher throughput than orthogonal multiple access (OMA). To update the state in evolutionary game for hybrid uplink NOMA, the replicator dynamic equation is considered with two possible implementation methods.
SNITCH: Dynamic Dependent Information Flow Analysis for Independent Java Bytecode
Venue: arXiv cs.SE 2019
Year: 2019

Software testing is the most commonly used technique in the industry to certify the correctness of software systems. This includes security properties like access control and data confidentiality. However, information flow control and the detection of information leaks using tests is a demanding task without the use of specialized monitoring and assessment tools. In this paper, we tackle the challenge of dynamically tracking information flow in third-party Java-based applications using dependent information flow control. Dependent security labels increase the expressiveness of traditional information flow control techniques by allowing to parametrize labels with context-related information and allowing for the specification of more detailed and fine-grained policies. Instead of the fixed security lattice used in traditional approaches that defines a fixed set of security compartments, dependent security labels allow for a dynamic lattice that can be extended at runtime, allowing for new security compartments to be defined using context values. We present a specification and instrumentation approach for rewriting JVM compiled code with in-lined reference monitors. To illustrate the proposed approach we use an example and a working prototype, SNITCH. SNITCH operates over the static single assignment language Shimple, an intermediate representation for Java bytecode used in the SOOT framework.
Distributed Consistent Network Updates in SDNs: Local Verification for Global Guarantees
Venue: arXiv cs.DC 2019
Year: 2019

While SDNs enable more flexible and adaptive network operations, (logically) centralized reconfigurations introduce overheads and delays, which can limit network reactivity. This paper initiates the study of a more distributed approach, in which the consistent network updates are implemented by the switches and routers directly in the data plane. In particular, our approach leverages concepts from local proof labeling systems, which allows the data plane elements to locally check network properties, and we show that this is sufficient to obtain global network guarantees. We demonstrate our approach considering three fundamental use cases, and analyze its benefits in terms of performance and fault-tolerance.
Multi-Layer Softmaxing during Training Neural Machine Translation for Flexible Decoding with Fewer Layers
Venue: arXiv cs.CL 2019
Year: 2019

This paper proposes a novel procedure for training an encoder-decoder based deep neural network which compresses NxM models into a single model enabling us to dynamically choose the number of encoder and decoder layers for decoding. Usually, the output of the last layer of the N-layer encoder is fed to the M-layer decoder, and the output of the last decoder layer is used to compute softmax loss. Instead, our method computes a single loss consisting of NxM losses: the softmax loss for the output of each of the M decoder layers derived using the output of each of the N encoder layers. A single model trained by our method can be used for decoding with an arbitrary fewer number of encoder and decoder layers. In practical scenarios, this (a) enables faster decoding with insignificant losses in translation quality and (b) alleviates the need to train NxM models, thereby saving space. We take a case study of neural machine translation and show the advantage and give a cost-benefit analysis of our approach.
Enabling Hyper-Personalisation: Automated Ad Creative Generation and Ranking for Fashion e-Commerce
Venue: arXiv cs.IR 2019
Year: 2019

Homepage is the first touch point in the customer's journey and is one of the prominent channels of revenue for many e-commerce companies. A user's attention is mostly captured by homepage banner images (also called Ads/Creatives). The set of banners shown and their design, influence the customer's interest and plays a key role in optimizing the click through rates of the banners. Presently, massive and repetitive effort is put in, to manually create aesthetically pleasing banner images. Due to the large amount of time and effort involved in this process, only a small set of banners are made live at any point. This reduces the number of banners created as well as the degree of personalization that can be achieved. This paper thus presents a method to generate creatives automatically on a large scale in a short duration. The availability of diverse banners generated helps in improving personalization as they can cater to the taste of larger audience. The focus of our paper is on generating wide variety of homepage banners that can be made as an input for user level personalization engine. Following are the main contributions of this paper: 1) We introduce and explain the need for large scale banner generation for e-commerce 2) We present on how we utilize existing deep learning based detectors which can automatically annotate the required objects/tags from the image. 3) We also propose a Genetic Algorithm based method to generate an optimal banner layout for the given image content, input components and other design constraints. 4) Further, to aid the process of picking the right set of banners, we designed a ranking method and evaluated multiple models. All our experiments have been performed on data from Myntra (http://www.myntra.com), one of the top fashion e-commerce players in India.
Learning Very Large Graphs with Unknown Vertex Distributions
Venue: arXiv cs.DS 2019
Year: 2019

Recently, Goldreich introduced the notion of property testing of bounded-degree graphs with an unknown distribution. We propose a slight modification of his idea: the Radon-Nikodym Oracles. Using these oracles any reasonable graph property can be tested in constant-time against any reasonable unknown distribution in the category of planar graphs. We also discuss Randomized Local Distributed Algorithms, which work on very large graphs with unknown distributions. Finally, we discuss how can we learn graph properties using observations instead of samplings.
Datalog Reasoning over Compressed RDF Knowledge Bases
Venue: arXiv cs.DB 2019
Year: 2019

Materialisation is often used in RDF systems as a preprocessing step to derive all facts implied by given RDF triples and rules. Although widely used, materialisation considers all possible rule applications and can use a lot of memory for storing the derived facts, which can hinder performance. We present a novel materialisation technique that compresses the RDF triples so that the rules can sometimes be applied to multiple facts at once, and the derived facts can be represented using structure sharing. Our technique can thus require less space, as well as skip certain rule applications. Our experiments show that our technique can be very effective: when the rules are relatively simple, our system is both faster and requires less memory than prominent state-of-the-art RDF systems.